151–160 of 297 posts
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But that is naive, isn't it? Who has the money and time in their life, to actually sue MS? Even if "you" is a business, few will have the resources for that.
Individuals do not (although a class action would be feasible), but large companies that use Github and other Microsoft products, of course they have both the means to sue Microsoft and the motivation should their business be impacted.
If somebody thinks an LLM is coming for everybody's coding job, I'd say this article is a great counterpoint just for existing. You could tell someone from decades ago that we now use a very high level language for complex tasks in complex code ecosystems, never even mention AI, explain that the parser is really generalist-biased, and this article would make perfect sense as an example of exemplary code by a modern c…
It's the same sort of thing you see happening with the customers of psychics. People often have poor awareness of how much they're putting in to a conversation. Or it's a bit like the way Tom Sawyer tricks other kids into painting the fence for him. For me a lot of the magic here is in knowing what questions to ask and when the answers aren't right. If you have those skills, is pounding out the code that hard?
The interesting part for me is not generating new bits of code, but the long-term maintenance of a whole thing. A while back there was a fashion for coding "wizards", things that would ask some questions and then generate code for you. People were very excited, as they saw it as lowering the barrier to entry. But the fashion died out because it just pushed all the problems a bit further down the road. Now you had novice developers trying to understand and improve code they weren't competent to write.
I suspect that in practice, anything a person can get a LLM to wholly write is also something that could be turned into a library or framework or service or no-code tool that they can just use. That, basically, if the novelty is low enough that an LLM can produce it, the novelty is low enough that there are better options than writing the code from scratch over and over.
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The opposite might be true, and here’s why - 1) by using English as spec, the barrier of entry has gone lower, 2) LLMs can also write prompts and self introspect to debug.
English as a spec is incredibly "fuzzy", there are many valid interpretations of intent. I don't think that can be avoided?
"I tried it and it didn't do what I wanted, not impressed."
My suggestion is to tune out the noise and really try experimenting with these tools – and know that they're rapidly improving. Even if ultimately you have criticisms or decide one way or another, at least really investigate them for your own use-cases rather than jumping on a bandwagon that's either "AI is bad" or the breathless hype-machine at the other end.
There's an unfortunately common take on AI that goes basically like this: "I tried it and it didn't do what I wanted, not impressed." My suggestion is to tune out the noise and really try experimenting with these tools – and know that they're rapidly improving. Even if ultimately you have criticisms or decide one way or another, at least really investigate them for your own use-cases rather than jumping on a bandwago…
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But you can't determine if a statement is true by simply reading more words. It's also not efficient for doing higher level work. There was a time before we had algebra where people were still expressing the same ideas but the notation wasn't there. Mathematics was expressed in "plain language." It's extremely difficult to read for us. For mathematician's of the time there was no other way to explain algorithms or ex…
Arguably reading code can’t lead to definitive conclusions about its bug-free-ness
If somebody thinks an LLM is coming for everybody's coding job, I'd say this article is a great counterpoint just for existing. You could tell someone from decades ago that we now use a very high level language for complex tasks in complex code ecosystems, never even mention AI, explain that the parser is really generalist-biased, and this article would make perfect sense as an example of exemplary code by a modern c…
Yeah, I think there's a "stone soup" effect going on with AI. It's the same sort of thing you see happening with the customers of psychics. People often have poor awareness of how much they're putting in to a conversation. Or it's a bit like the way Tom Sawyer tricks other kids into painting the fence for him. For me a lot of the magic here is in knowing what questions to ask and when the answers aren't right. If you…
This is a bot tangential - your reference to stone soup is a wonderful example of the information density possible with natural language. And all the meaning and story behind the phrase is accessible to LLMs.
I’ll have to start experimenting with idiom driven development, especially when prompt golfing.
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The opposite might be true, and here’s why - 1) by using English as spec, the barrier of entry has gone lower, 2) LLMs can also write prompts and self introspect to debug.
I think English as a spec actually makes the barrier of entry higher, not lower. Code itself is far easier to understand than an English description of the code. To understand an English description of code you already have to have a deeper understanding of what the code is doing. For code itself you can reference the syntax to understand what's going on. The prompt in this case is using very technical language that…